*Result*: Advancing Pulmonary Embolism Detection with Integrated Deep Learning Architectures.
Title:
Advancing Pulmonary Embolism Detection with Integrated Deep Learning Architectures.
Authors:
Biret CB; Department of Emergency Medicine, College of Medicine, Inonu University, Malatya, Turkey., Gurbuz S; Department of Emergency Medicine, College of Medicine, Inonu University, Malatya, Turkey., Akbal E; Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey., Baygin M; Department of Computer Engineering, College of Engineering, Erzurum Technical University, Erzurum, Turkey., Ekingen E; Republic of Turkey Ministry of Health Antalya Provincial Health Directorate, Antalya, Turkey., Derya S; Department of Emergency Medicine, College of Medicine, Inonu University, Malatya, Turkey., Yıldırım IO; Department of Radiology, College of Medicine, Inonu University, Malatya, Turkey., Sercek I; Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey., Dogan S; Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey. sdogan@firat.edu.tr., Tuncer T; Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey.
Source:
Journal of imaging informatics in medicine [J Imaging Inform Med] 2026 Feb; Vol. 39 (1), pp. 186-201. Date of Electronic Publication: 2025 Apr 25.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Springer Nature Country of Publication: Switzerland NLM ID: 9918663679206676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2948-2933 (Electronic) Linking ISSN: 29482925 NLM ISO Abbreviation: J Imaging Inform Med Subsets: MEDLINE
Imprint Name(s):
Original Publication: [Cham, Switzerland] : Springer Nature, [2024]-
MeSH Terms:
References:
Vadhera R, Sharma M: Optimizing Pulmonary Embolism detection through diverse UNET architectural variations. International Journal of Computing and Digital Systems 16:1-21, 2024.
Yadlapalli P, Teja AL, Raju CMA, Reddy KSS, Mithra K, Dokku B: Segmentation of pulmonary embolism using deep learning: City, 2022 Year.
Frank C, Ratchford EV, Moll S: Vascular Disease Patient Information Page: A guide for patients with newly diagnosed deep vein thrombosis or pulmonary embolism. Vascular Medicine 28:481-486, 2023. (PMID: 10.1177/1358863X2311547563740177110559639)
Mirakhorli F, Vahidi B, Pazouki M, Barmi PT: A fluid-structure interaction analysis of blood clot motion in a branch of pulmonary arteries. Cardiovascular Engineering and Technology 14:79-91, 2023. (PMID: 10.1007/s13239-022-00632-435788909)
Chernysh IN, Spiewak R, Cambor CL, Purohit PK, Weisel JW: Structure, mechanical properties, and modeling of cyclically compressed pulmonary emboli. Journal of the mechanical behavior of biomedical materials 105:103699, 2020. (PMID: 10.1016/j.jmbbm.2020.103699322798467241098)
Rosovsky RP, et al.: Diagnosis and treatment of pulmonary embolism during the coronavirus disease 2019 pandemic: a position paper from the National PERT Consortium. Chest 158:2590-2601, 2020. (PMID: 10.1016/j.chest.2020.08.2064328616927450258)
Willetts J, Chapter WT: Cardiovascular Presentations. Primary Care for Paramedics, 2023.
Hillegass E, Lukaszewicz K, Puthoff M: Role of physical therapists in the management of individuals at risk for or diagnosed with venous thromboembolism: evidence-based clinical practice guideline 2022. Physical Therapy 102:pzac057, 2022.
Wilson E, Phair J, Carnevale M, Koleilat I: Common reasons for malpractice lawsuits involving pulmonary embolism and deep vein thrombosis. Journal of Surgical Research 245:212-216, 2020. (PMID: 10.1016/j.jss.2019.07.07931421365)
Wenger N, Sebastian T, Engelberger RP, Kucher N, Spirk D: Pulmonary embolism and deep vein thrombosis: similar but different. Thrombosis research 206:88-98, 2021. (PMID: 10.1016/j.thromres.2021.08.01534454241)
Shiri P, Taghipour L, Gharebagh MR, Shamsaldini AR, Masoumi M: Physical and mental problem in pregnant and adult women with heart disease based on psychology and nursing education by examining the risk of vascular embolism in these patients: A Systematic Review. International Neurourology Journal 28:461-470, 2024.
Hotoleanu C: Association between obesity and venous thromboembolism. Medicine and pharmacy reports 93:162, 2020. (PMID: 10.15386/mpr-1372324783227243888)
Freund Y, Cohen-Aubart F, Bloom B: Acute pulmonary embolism: a review. Jama 328:1336-1345, 2022. (PMID: 36194215)
Ishaaya E, Tapson VF: Advances in the diagnosis of acute pulmonary embolism. F1000Research 9, 2020.
Bodnar P, Bodnar Y, Bodnar T, Bodnar L, Hvalyboha D: Justification of using Computed Tomography and Magnetic Resonance Imaging for Deep Venous Thrombosis and Pulmonary Embolism. International Journal of Online & Biomedical Engineering 17, 2021.
Falsetti L, et al.: Cutting-Edge Techniques and Drugs for the Treatment of Pulmonary Embolism: Current Knowledge and Future Perspectives. Journal of Clinical Medicine 13:1952, 2024. (PMID: 10.3390/jcm130719523861071711012374)
Licha CRM, McCurdy CM, Maldonado SM, Lee LS: Current management of acute pulmonary embolism. Annals of Thoracic and Cardiovascular Surgery 26:65-71, 2020. (PMID: 10.5761/atcs.ra.19-00158)
Righini M, Robert‐Ebadi H, Le Gal G: Diagnosis of acute pulmonary embolism. Journal of Thrombosis and Haemostasis 15:1251-1261, 2017. (PMID: 10.1111/jth.1369428671347)
Kaplan E, Dogan S, Tuncer T, Baygin M, Altunisik E: Feed-forward LPQNet based automatic alzheimer’s disease detection model. Computers in Biology and Medicine 137:104828, 2021. (PMID: 10.1016/j.compbiomed.2021.10482834507154)
Santos MK, Ferreira JR, Wada DT, Tenório APM, Nogueira-Barbosa MH, Marques PMdA: Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine. Radiologia brasileira 52:387-396, 2019. (PMID: 10.1590/0100-3984.2019.0049320473337007049)
Bushra F, et al.: Deep learning in computed tomography pulmonary angiography imaging: A dual-pronged approach for pulmonary embolism detection. Expert Systems with Applications 245:123029, 2024. (PMID: 10.1016/j.eswa.2023.123029)
Masoudi M, Pourreza H-R, Saadatmand-Tarzjan M, Eftekhari N, Zargar FS, Rad MP: A new dataset of computed-tomography angiography images for computer-aided detection of pulmonary embolism. Scientific data 5:1-9, 2018. (PMID: 10.1038/sdata.2018.180)
Sukumar S, Harish A, Shahina A, Sanjana B, Khan AN: Deep Learning based Pulmonary Embolism Detection using Convolutional Feature Maps of CT Pulmonary Angiography Images. Procedia Computer Science 233:317-326, 2024. (PMID: 10.1016/j.procs.2024.03.221)
Colak E, et al.: The RSNA pulmonary embolism CT dataset. Radiology: Artificial Intelligence 3:e200254, 2021.
Olescki G, Clementin de Andrade JMC, Escuissato DL, Oliveira LF: A two step workflow for pulmonary embolism detection using deep learning and feature extraction. Computer methods in biomechanics and biomedical engineering: imaging & visualization 11:341-350, 2023.
González G, et al.: Computer aided detection for pulmonary embolism challenge (CAD-PE). arXiv preprint arXiv:200313440, 2020.
Grenier PA, et al.: Deep learning-based algorithm for automatic detection of pulmonary embolism in chest CT angiograms. Diagnostics 13:1324, 2023. (PMID: 10.3390/diagnostics130713243704654210093638)
Xu H, et al.: Automatic detection of pulmonary embolism in computed tomography pulmonary angiography using Scaled‐YOLOv4. Medical Physics 50:4340-4350, 2023. (PMID: 10.1002/mp.1621836633186)
Hemalakshmi GR, Murugappan M, Sikkandar MY, Santhi D, Prakash NB, Mohanarathinam A: PE-Ynet: a novel attention-based multi-task model for pulmonary embolism detection using CT pulmonary angiography (CTPA) scan images. Physical and Engineering Sciences in Medicine:1–18, 2024.
Chen Y, et al.: Scunet++: Swin-unet and cnn bottleneck hybrid architecture with multi-fusion dense skip connection for pulmonary embolism ct image segmentation: City, 2024 Year.
Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C: Mobilenetv2: Inverted residuals and linear bottlenecks: City, 2018 Year.
He K, Zhang X, Ren S, Sun J: Deep residual learning for image recognition: City, 2016 Year.
Liu Z, Mao H, Wu C-Y, Feichtenhofer C, Darrell T, Xie S: A convnet for the 2020s: City, 2022 Year.
Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint arXiv:160207360, 2016.
Tuncer T, Dogan S, Pławiak P, Acharya UR: Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals. Knowledge-Based Systems 186:104923, 2019. (PMID: 10.1016/j.knosys.2019.104923)
Tuncer T, Dogan S, Acharya UR: Automated accurate speech emotion recognition system using twine shuffle pattern and iterative neighborhood component analysis techniques. Knowledge-Based Systems 211:106547, 2021. (PMID: 10.1016/j.knosys.2020.106547)
Peterson LE: K-nearest neighbor. Scholarpedia 4:1883, 2009. (PMID: 10.4249/scholarpedia.1883)
Safavian SR, Landgrebe D: A survey of decision tree classifier methodology. IEEE transactions on systems, man, and cybernetics 21:660-674, 1991. (PMID: 10.1109/21.97458)
Zhao W, Chellappa R, Nandhakumar N: Empirical performance analysis of linear discriminant classifiers. Proc. Proceedings 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat No 98CB36231): City.
Bhattacharyya S, Khasnobish A, Chatterjee S, Konar A, Tibarewala D: Performance analysis of LDA, QDA and KNN algorithms in left-right limb movement classification from EEG data. Proc. 2010 International conference on systems in medicine and biology: City.
Ng AY, Jordan MI: On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. Proc. Advances in neural information processing systems: City.
Vapnik V: The support vector method of function estimation: Springer, 1998.
Maillo J, Ramírez S, Triguero I, Herrera F: kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data. Knowledge-Based Systems 117:3-15, 2017. (PMID: 10.1016/j.knosys.2016.06.012)
Hothorn T, Lausen B: Bagging tree classifiers for laser scanning images: a data-and simulation-based strategy. Artificial intelligence in medicine 27:65-79, 2003. (PMID: 10.1016/S0933-3657(02)00085-412473392)
Saxena A, Saad A: Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems. Applied Soft Computing 7:441-454, 2007. (PMID: 10.1016/j.asoc.2005.10.001)
Efron B: The efficiency of logistic regression compared to normal discriminant analysis. Journal of the American Statistical Association 70:892-898, 1975. (PMID: 10.1080/01621459.1975.10480319)
Huhtanen H, Nyman M, Mohsen T, Virkki A, Karlsson A, Hirvonen J: Automated detection of pulmonary embolism from CT-angiograms using deep learning. BMC Medical Imaging 22:43, 2022. (PMID: 10.1186/s12880-022-00763-z352828218919639)
Ma X, Ferguson EC, Jiang X, Savitz SI, Shams S: A multitask deep learning approach for pulmonary embolism detection and identification. Scientific Reports 12:13087, 2022. (PMID: 10.1038/s41598-022-16976-9359064779338063)
Akilandeswaria J, Jothib G, Naveenkumara A, Sabeenianc R, Iyyanara P, Paramasivamc M: Detecting pulmonary embolism using deep neural networks. International Journal of Performability Engineering 17:322, 2021. (PMID: 10.23940/ijpe.21.03.p8.322332)
Lynch D, Suriya M: PE-DeepNet: A deep neural network model for pulmonary embolism detection. International Journal of Intelligent Networks 3:176-180, 2022. (PMID: 10.1016/j.ijin.2022.10.001)
Ajmera P, et al.: A deep learning approach for automated diagnosis of pulmonary embolism on computed tomographic pulmonary angiography. BMC Medical Imaging 22:195, 2022. (PMID: 10.1186/s12880-022-00916-0363689759651891)
Huang S-C, et al.: PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging. NPJ digital medicine 3:61, 2020. (PMID: 10.1038/s41746-020-0266-y323520397181770)
Pu J, et al.: Automated detection and segmentation of pulmonary embolisms on computed tomography pulmonary angiography (CTPA) using deep learning but without manual outlining. Medical Image Analysis 89:102882, 2023. (PMID: 10.1016/j.media.2023.1028823748203210528048)
Field A: Discovering statistics using IBM SPSS statistics: Sage publications limited, 2024.
Yadlapalli P, Teja AL, Raju CMA, Reddy KSS, Mithra K, Dokku B: Segmentation of pulmonary embolism using deep learning: City, 2022 Year.
Frank C, Ratchford EV, Moll S: Vascular Disease Patient Information Page: A guide for patients with newly diagnosed deep vein thrombosis or pulmonary embolism. Vascular Medicine 28:481-486, 2023. (PMID: 10.1177/1358863X2311547563740177110559639)
Mirakhorli F, Vahidi B, Pazouki M, Barmi PT: A fluid-structure interaction analysis of blood clot motion in a branch of pulmonary arteries. Cardiovascular Engineering and Technology 14:79-91, 2023. (PMID: 10.1007/s13239-022-00632-435788909)
Chernysh IN, Spiewak R, Cambor CL, Purohit PK, Weisel JW: Structure, mechanical properties, and modeling of cyclically compressed pulmonary emboli. Journal of the mechanical behavior of biomedical materials 105:103699, 2020. (PMID: 10.1016/j.jmbbm.2020.103699322798467241098)
Rosovsky RP, et al.: Diagnosis and treatment of pulmonary embolism during the coronavirus disease 2019 pandemic: a position paper from the National PERT Consortium. Chest 158:2590-2601, 2020. (PMID: 10.1016/j.chest.2020.08.2064328616927450258)
Willetts J, Chapter WT: Cardiovascular Presentations. Primary Care for Paramedics, 2023.
Hillegass E, Lukaszewicz K, Puthoff M: Role of physical therapists in the management of individuals at risk for or diagnosed with venous thromboembolism: evidence-based clinical practice guideline 2022. Physical Therapy 102:pzac057, 2022.
Wilson E, Phair J, Carnevale M, Koleilat I: Common reasons for malpractice lawsuits involving pulmonary embolism and deep vein thrombosis. Journal of Surgical Research 245:212-216, 2020. (PMID: 10.1016/j.jss.2019.07.07931421365)
Wenger N, Sebastian T, Engelberger RP, Kucher N, Spirk D: Pulmonary embolism and deep vein thrombosis: similar but different. Thrombosis research 206:88-98, 2021. (PMID: 10.1016/j.thromres.2021.08.01534454241)
Shiri P, Taghipour L, Gharebagh MR, Shamsaldini AR, Masoumi M: Physical and mental problem in pregnant and adult women with heart disease based on psychology and nursing education by examining the risk of vascular embolism in these patients: A Systematic Review. International Neurourology Journal 28:461-470, 2024.
Hotoleanu C: Association between obesity and venous thromboembolism. Medicine and pharmacy reports 93:162, 2020. (PMID: 10.15386/mpr-1372324783227243888)
Freund Y, Cohen-Aubart F, Bloom B: Acute pulmonary embolism: a review. Jama 328:1336-1345, 2022. (PMID: 36194215)
Ishaaya E, Tapson VF: Advances in the diagnosis of acute pulmonary embolism. F1000Research 9, 2020.
Bodnar P, Bodnar Y, Bodnar T, Bodnar L, Hvalyboha D: Justification of using Computed Tomography and Magnetic Resonance Imaging for Deep Venous Thrombosis and Pulmonary Embolism. International Journal of Online & Biomedical Engineering 17, 2021.
Falsetti L, et al.: Cutting-Edge Techniques and Drugs for the Treatment of Pulmonary Embolism: Current Knowledge and Future Perspectives. Journal of Clinical Medicine 13:1952, 2024. (PMID: 10.3390/jcm130719523861071711012374)
Licha CRM, McCurdy CM, Maldonado SM, Lee LS: Current management of acute pulmonary embolism. Annals of Thoracic and Cardiovascular Surgery 26:65-71, 2020. (PMID: 10.5761/atcs.ra.19-00158)
Righini M, Robert‐Ebadi H, Le Gal G: Diagnosis of acute pulmonary embolism. Journal of Thrombosis and Haemostasis 15:1251-1261, 2017. (PMID: 10.1111/jth.1369428671347)
Kaplan E, Dogan S, Tuncer T, Baygin M, Altunisik E: Feed-forward LPQNet based automatic alzheimer’s disease detection model. Computers in Biology and Medicine 137:104828, 2021. (PMID: 10.1016/j.compbiomed.2021.10482834507154)
Santos MK, Ferreira JR, Wada DT, Tenório APM, Nogueira-Barbosa MH, Marques PMdA: Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine. Radiologia brasileira 52:387-396, 2019. (PMID: 10.1590/0100-3984.2019.0049320473337007049)
Bushra F, et al.: Deep learning in computed tomography pulmonary angiography imaging: A dual-pronged approach for pulmonary embolism detection. Expert Systems with Applications 245:123029, 2024. (PMID: 10.1016/j.eswa.2023.123029)
Masoudi M, Pourreza H-R, Saadatmand-Tarzjan M, Eftekhari N, Zargar FS, Rad MP: A new dataset of computed-tomography angiography images for computer-aided detection of pulmonary embolism. Scientific data 5:1-9, 2018. (PMID: 10.1038/sdata.2018.180)
Sukumar S, Harish A, Shahina A, Sanjana B, Khan AN: Deep Learning based Pulmonary Embolism Detection using Convolutional Feature Maps of CT Pulmonary Angiography Images. Procedia Computer Science 233:317-326, 2024. (PMID: 10.1016/j.procs.2024.03.221)
Colak E, et al.: The RSNA pulmonary embolism CT dataset. Radiology: Artificial Intelligence 3:e200254, 2021.
Olescki G, Clementin de Andrade JMC, Escuissato DL, Oliveira LF: A two step workflow for pulmonary embolism detection using deep learning and feature extraction. Computer methods in biomechanics and biomedical engineering: imaging & visualization 11:341-350, 2023.
González G, et al.: Computer aided detection for pulmonary embolism challenge (CAD-PE). arXiv preprint arXiv:200313440, 2020.
Grenier PA, et al.: Deep learning-based algorithm for automatic detection of pulmonary embolism in chest CT angiograms. Diagnostics 13:1324, 2023. (PMID: 10.3390/diagnostics130713243704654210093638)
Xu H, et al.: Automatic detection of pulmonary embolism in computed tomography pulmonary angiography using Scaled‐YOLOv4. Medical Physics 50:4340-4350, 2023. (PMID: 10.1002/mp.1621836633186)
Hemalakshmi GR, Murugappan M, Sikkandar MY, Santhi D, Prakash NB, Mohanarathinam A: PE-Ynet: a novel attention-based multi-task model for pulmonary embolism detection using CT pulmonary angiography (CTPA) scan images. Physical and Engineering Sciences in Medicine:1–18, 2024.
Chen Y, et al.: Scunet++: Swin-unet and cnn bottleneck hybrid architecture with multi-fusion dense skip connection for pulmonary embolism ct image segmentation: City, 2024 Year.
Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C: Mobilenetv2: Inverted residuals and linear bottlenecks: City, 2018 Year.
He K, Zhang X, Ren S, Sun J: Deep residual learning for image recognition: City, 2016 Year.
Liu Z, Mao H, Wu C-Y, Feichtenhofer C, Darrell T, Xie S: A convnet for the 2020s: City, 2022 Year.
Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint arXiv:160207360, 2016.
Tuncer T, Dogan S, Pławiak P, Acharya UR: Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals. Knowledge-Based Systems 186:104923, 2019. (PMID: 10.1016/j.knosys.2019.104923)
Tuncer T, Dogan S, Acharya UR: Automated accurate speech emotion recognition system using twine shuffle pattern and iterative neighborhood component analysis techniques. Knowledge-Based Systems 211:106547, 2021. (PMID: 10.1016/j.knosys.2020.106547)
Peterson LE: K-nearest neighbor. Scholarpedia 4:1883, 2009. (PMID: 10.4249/scholarpedia.1883)
Safavian SR, Landgrebe D: A survey of decision tree classifier methodology. IEEE transactions on systems, man, and cybernetics 21:660-674, 1991. (PMID: 10.1109/21.97458)
Zhao W, Chellappa R, Nandhakumar N: Empirical performance analysis of linear discriminant classifiers. Proc. Proceedings 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat No 98CB36231): City.
Bhattacharyya S, Khasnobish A, Chatterjee S, Konar A, Tibarewala D: Performance analysis of LDA, QDA and KNN algorithms in left-right limb movement classification from EEG data. Proc. 2010 International conference on systems in medicine and biology: City.
Ng AY, Jordan MI: On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. Proc. Advances in neural information processing systems: City.
Vapnik V: The support vector method of function estimation: Springer, 1998.
Maillo J, Ramírez S, Triguero I, Herrera F: kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data. Knowledge-Based Systems 117:3-15, 2017. (PMID: 10.1016/j.knosys.2016.06.012)
Hothorn T, Lausen B: Bagging tree classifiers for laser scanning images: a data-and simulation-based strategy. Artificial intelligence in medicine 27:65-79, 2003. (PMID: 10.1016/S0933-3657(02)00085-412473392)
Saxena A, Saad A: Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems. Applied Soft Computing 7:441-454, 2007. (PMID: 10.1016/j.asoc.2005.10.001)
Efron B: The efficiency of logistic regression compared to normal discriminant analysis. Journal of the American Statistical Association 70:892-898, 1975. (PMID: 10.1080/01621459.1975.10480319)
Huhtanen H, Nyman M, Mohsen T, Virkki A, Karlsson A, Hirvonen J: Automated detection of pulmonary embolism from CT-angiograms using deep learning. BMC Medical Imaging 22:43, 2022. (PMID: 10.1186/s12880-022-00763-z352828218919639)
Ma X, Ferguson EC, Jiang X, Savitz SI, Shams S: A multitask deep learning approach for pulmonary embolism detection and identification. Scientific Reports 12:13087, 2022. (PMID: 10.1038/s41598-022-16976-9359064779338063)
Akilandeswaria J, Jothib G, Naveenkumara A, Sabeenianc R, Iyyanara P, Paramasivamc M: Detecting pulmonary embolism using deep neural networks. International Journal of Performability Engineering 17:322, 2021. (PMID: 10.23940/ijpe.21.03.p8.322332)
Lynch D, Suriya M: PE-DeepNet: A deep neural network model for pulmonary embolism detection. International Journal of Intelligent Networks 3:176-180, 2022. (PMID: 10.1016/j.ijin.2022.10.001)
Ajmera P, et al.: A deep learning approach for automated diagnosis of pulmonary embolism on computed tomographic pulmonary angiography. BMC Medical Imaging 22:195, 2022. (PMID: 10.1186/s12880-022-00916-0363689759651891)
Huang S-C, et al.: PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging. NPJ digital medicine 3:61, 2020. (PMID: 10.1038/s41746-020-0266-y323520397181770)
Pu J, et al.: Automated detection and segmentation of pulmonary embolisms on computed tomography pulmonary angiography (CTPA) using deep learning but without manual outlining. Medical Image Analysis 89:102882, 2023. (PMID: 10.1016/j.media.2023.1028823748203210528048)
Field A: Discovering statistics using IBM SPSS statistics: Sage publications limited, 2024.
Contributed Indexing:
Keywords: Deep feature engineering; HybridNeXt; INCA; Pulmonary embolism detection; Self-organized model
Entry Date(s):
Date Created: 20250425 Date Completed: 20260219 Latest Revision: 20260222
Update Code:
20260222
PubMed Central ID:
PMC12921004
DOI:
10.1007/s10278-025-01506-6
PMID:
40281216
Database:
MEDLINE
*Further Information*
*The main aim of this study is to introduce a new hybrid deep learning model for biomedical image classification. We propose a novel convolutional neural network (CNN), named HybridNeXt, for detecting pulmonary embolism (PE) from computed tomography (CT) images. To evaluate the HybridNeXt model, we created a new dataset consisting of two classes: (1) PE and (2) control. The HybridNeXt architecture combines different advanced CNN blocks, including MobileNet, ResNet, ConvNeXt, and Swin Transformer. We specifically designed this model to combine the strengths of these well-known CNNs. The architecture also includes stem, downsampling, and output stages. By adjusting the parameters, we developed a lightweight version of HybridNeXt, suitable for clinical use. To further improve the classification performance and demonstrate transfer learning capability, we proposed a deep feature engineering (DFE) method using a multilevel discrete wavelet transform (MDWT). This DFE model has three main phases: (i) feature extraction from raw images and wavelet bands, (ii) feature selection using iterative neighborhood component analysis (INCA), and (iii) classification using a k-nearest neighbors (kNN) classifier. We first trained HybridNeXt on the training images, creating a pretrained HybridNeXt model. Then, using this pretrained model, we extracted features and applied the proposed DFE method for classification. The HybridNeXt model achieved a test accuracy of 90.14%, while our DFE model improved accuracy to 96.35%. Overall, the results confirm that our HybridNeXt architecture is highly accurate and effective for biomedical image classification. The presented HybridNeXt and HybridNeXt-based DFE methods can potentially be applied to other image classification tasks.
(© 2025. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)*
*Declarations. Ethics Approval: This research was approved on ethical grounds by the Non-Invasive Ethics Committee, Inonu University Training and Research Hospital, on October 04, 2023 (2023/3915). Consent to Participate: Informed consent was obtained from all subjects involved in the study. Consent for Publication: All authors have agreed with the final version of the manuscript for publication. Conflict of Interest: The authors declare no competing interests.*